Fall detection and fall risk detection systems and methods

ABSTRACT

The present invention relates to a light-weight, small and portable ambulatory sensor for measuring and monitoring a person&#39;s physical activity. Based on these measurements and computations, the invented system quantifies the subject&#39;s physical activity, quantifies the subject&#39;s gait, determines his or her risk of falling, and automatically detects falls. The invention combines the features of portability, high autonomy, and real-time computational capacity. High autonomy is achieved by using only accelerometers, which have low power consumption rates as compared with gyroscope-based systems. Accelerometer measurements, however, contain significant amounts of noise, which must be removed before further analysis. The invention therefore uses novel time-frequency filters to denoise the measurements, and in conjunction with biomechanical models of human movement, perform the requisite computations, which may also be done in real time.

CROSS-REFERENCE TO RELATED APPLICATIONS

Any and all applications for which a foreign or domestic priority claimis identified in the Application Data Sheet as filed with the presentapplication are incorporated by reference under 37 CFR 1.57 and made apart of this specification.

FIELD

This invention generally relates to body movement monitoring systems,specifically to an ambulatory system which (1) measures and quantifiesparameters related to the user's postures and movements; (2) evaluatesthe user's risk of falling; and (3) automatically detects the user'sfalls.

BACKGROUND OF THE INVENTION

We envision several uses for the present invention. In the fields ofelderly care and physical therapy, the present invention finds severalimportant uses. We envision that the invented system can provide bothqualitative and quantitative monitoring of an elderly person's physicalactivity (PA) during his or her everyday life. This information isuseful for several reasons: first, PA monitoring can accuratelydetermine the user's state of physical and mental health, identifyingsubacute changes in their health status. For example, this system candetect early deteriorations in the amount and quality of the subjects'PA due to various health conditions (e.g., congestive heart failure,development of infections, etc.) Second, PA monitoring provides valuableinformation about the sequence of the elderly person's movements duringthe time window surrounding their falls. This information significantlyaids the development of alert systems to predict, and ideally, preventfall occurrences. Third, assessment of the effects of new drugs and paintreatments are significantly advanced through monitoring of thesubjects' physical activity during his or her everyday life. Fourth,monitoring of PA in the elderly population can, over time, provideinsight into qualitative and quantitative changes in PA as a result ofall adverse physical events, such as functional declines orhospitalizations. Persons at risk can therefore be identified, and novelpreventive interventional methods may be tailored to their needs. Theinvented system also finds use in remote monitoring and telecare ofpeople suffering from various diseases, such as Alzheimer's, as well asof those recovering and rehabilitating from diseases and medicalprocedures.

In clinical research and studies, the invented system provides valuableinsight into the mechanisms and factors influencing physical activityand balance by quantifying the subject's PA and risk of falling (RoF) inall contexts, including everyday life.

In drug development, the invented system can be used to study the roleof various drugs and treatment procedures on the physical activity andRoF of people during clinical studies.

In athletics training, this system provides valuable feedback on theuser's body movements, and can be a valuable tool for both training andon-field performance measurement.

Measurement and monitoring of PA by the present invented system alsofinds use in weight management by providing intelligent feedback to theuser about his or her daily energy expenditures.

Postural Transitions:

Najafi et al. [1-3] have developed algorithms for identifying posturaltransitions (PT), e.g., sit-to-stand (SI-ST) and stand-to-sit (ST-SI)from data recorded by a gyroscopic sensor attached to the subject'strunk. The high power-consumption rates of gyroscopes, however, severelylimits the applicability of these algorithms for applications outside ofthe laboratory (which include everyday life applications), since such asystem has an autonomy of only a few hours, therefore requiring frequentrecharging or exchanges of the battery. Although the addition of morebatteries would increase the device's autonomy, it will also increaseits size and weight, thus hindering the subject's natural movements.

By contrast, the algorithms developed as part of the present inventionuse accelerometer data in place of gyroscope data, and therefore enablelong-term, autonomous operability of the system.

Gait Analysis:

Proper gait function (i.e., quality of gait) requires the ability tomaintain safe gait while navigating in complex and changingenvironments, and to conform one's gait to different task demands.Furthermore, a person's quality of gait is closely linked to his or heroverall state of health. For example, walking speed correlates with theindividual's ability to live independently, with the ability to performvarious activities of daily life (such as safely crossing a trafficintersection), and with reductions in the risk of falling [4].

Since evaluation of a person's overall health and quality of life aregreatly facilitated by knowledge of his or her gait function duringeveryday life, a system that can automatically extract gait-relatedparameters with minimal hindrance of the user's movements is highlyuseful. To date, however, fully satisfactory methods and systems havenot been developed. Current techniques for computing a person's gaitparameters are primarily based on the use of vertical accelerometersignals, together with a peak-detection algorithm to identify thewalking step. Such techniques, however, possess several importantshortcomings.

First, they cannot remove the rotational artifacts generated by the bodysegment to which the sensor has been attached. These noise artifactsstem from the gravitational component of the accelerometer signal. Whilethey can be easily removed in the case of healthy young subjects, suchartifacts pose a key challenge to accurate computation of gaitparameters in the case of patients and the elderly-who tend to walkslowly and may use walking aids. Second, current algorithms cannotdiscriminate between acceleration peaks associated with posturaltransitions, and those due to walking steps, thus leading to very lowspecificity during activity daily life (ADL).

Alternative technologies for estimating the gait pattern usecombinations of gyroscopes and/or accelerometers attached to the lowerlimbs [5-7]. Use of gyroscopes decreases the autonomy of the system dueto high power consumption. Moreover, attaching the sensors on lowerlimbs hinders the user's movements, who must carry the system duringADL.

The present invention accurately identifies the user's walking periodsduring ADL, discriminates between left and right gait steps, andestimates the spatiotemporal parameters of gait (e.g., swing, stance,double support, and gait speed) using only accelerometers. Aminian etal. (1999) [7] have suggested an algorithm, based on a neural network,that extracts spatio-temporal parameters of gait using accelerometersattached to the subject's lower back. This algorithm, however, requiresa calibration/pre-learning stage that can only be accomplished by havingsubjects walk within a constrained space of a gait lab. This requirementrenders that algorithm impractical for use during everyday lifeactivities. By contrast, the algorithms developed as part of the presentinvention require no initial calibrations, and therefore can be easilyused by any individual.

In so doing, our algorithms overcome the shortcomings present in theprior art: the small, lightweight and portable sensory module, attachedto the subject's chest, poses minimal hindrance to his or her movementsduring ADL. Furthermore, the accelerometers consume considerably lesspower than do gyroscopes, leading to significantly longer operationaltimes. Moreover, the invented system provides significantly higheraccuracy in discriminations, and better removes rotational noiseartifacts.

Risk of Falling:

Evaluation of the individual's risk of falling is required in providingadapted assistance and preventive measures for subjects deemed at a highrisk of falling. This risk is generally evaluated by usingquestionnaires, which have shortcomings such as subjectivity and limitedaccuracy in recall [8]. Risk of falling can also be evaluated byclinical and functional tests, such as assessments of posture and gait,independence in daily life, cognition, and vision [9-10]. However, anobjective method for remotely monitoring this risk through themonitoring the daily physical activity (PA) has not yet been developed.By contrast, the present invention assesses and monitors the user's riskof falling through monitoring and measurement of his or her dailyphysical activity.

Automatic Fall Detection:

Of the health problems commonly associated with aging, the most seriousis falling-defined as a person's trunk, knee, or hand unintentionallycoming to rest on the ground or a lower level below the waist. Areliable system to remotely detect falls allows delivery of early careto these persons, and decreases the detrimental consequences of falls,leading to substantial health-care cost savings. Current fall alarmsystems require activation and are therefore inappropriate in falls dueto syncope, a loss of consciousness associated with cerebro-vascularaccidents. Moreover, persons suffering from Alzheimer'sdisease—affecting approximately one-third of persons aged 80 years andolder—may not be capable of activating such systems. A reliable systemcapable of sending automatic alarms when assistance is necessary willtherefore provide an innovative way to support these patients and theircaregivers. Automatic fall reporting would also be important in clinicalresearch to reliably record occurrence of falls.

Current detection of falls essentially relies on self-reporting andcomplex reporting systems with daily phone-call reminders. In fact, forthe research community interested in fall prevention, the documentationof falls is a methodological pitfall, and no unanimously accepted methodfor reporting falls exists. Little data support claims to thereliability and validity of different reporting systems. Oral reportshave many limitations due to the cognitive status of the subjects aswell as mental factors such as shame or fear of reporting. Finally, fallevents associated with loss of consciousness due to syncope, stroke orepileptic seizures are not always recognized.

While a number of different approaches to fall detection have appearedin recent years [11-14], they have primarily used patterns recorded bytri-axial accelerometers to identify shocks related to falls,independent of the previous posture (i.e. sitting, lying, standing)and/or the state of activity (e.g. rest, walking, turning, posturaltransition, etc) of the faller. Not using the key information about theperson's previous posture and state of activity likely gives rise tofalse detections, dramatically decreasing the accuracy of the falldetector. The present invention, by contrast, identifies falls with highsensitivity and specificity using only signals from accelerometers.

SUMMARY

The present invention consists of a body movement monitoring system thatincludes a sensing unit, attachable to the upper part of the user'sbody, such as trunk or shoulder, comprising a tri-axial accelerometer,or, three mono-axial accelerometers measuring accelerations in threeperpendicular directions. The system also includes one or more processorcircuits configured to: process the signals recorded by theaccelerometer(s) and derive information related to the subject'smovement from said accelerometer(s). Some or all of these analyses maybe carried out on-board the sensing unit. In all cases, software-basedalgorithms, developed as part of the present invention, are integratedwith the processor circuits performing the analyses. One or more datastorage systems are also included in the system, and are configured tostore signals recorded by said accelerometer(s), or the informationderived by one of said processor circuits, or both. One or more of saiddata storage systems may be housed within said sensor. An optionalcommunications system, configured to transmit at least a portion of thedata recorded by said accelerometers, or at least a portion of theinformation derived by said the processor circuit housed within thesensor, or both, may also be housed with the sensor. The informationderived from the measured acceleration signals are used to monitor andquantify the user's physical activity; automatically detect the user'srisk of falling; and assess the user's risk of falling. The requiredcomputations are performed according to software-based algorithms,developed as part of the present invention, which use at least onebiomechanical model of human body movement, and one or more signalprocessing time-frequency filters.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, and advantages of theinvention will be apparent from the following detailed description ofthe invention, as illustrated in the accompanying drawings, in whichlike reference numerals designate like parts throughout the figuresthereof and wherein:

FIG. 1a illustrates how an elderly subject may wear the sensory module,and also shows the three components of acceleration measured by thesensory unit;

FIG. 1b is a two-dimensional schematic of a subject wearing the sensoryunit, and shows the subject's trunk lean angle θ, the direction ofgravity, as well as the frontal and vertical acceleration components;

FIG. 2 is a flowchart of the algorithms used to determine the time, timeand duration of the subject's postural transitions;

FIGS. 3a-f demonstrate the operation of the algorithms in determiningthe time, type and duration of the subject's postural transitions;

FIG. 4 is a flowchart of the algorithms used to identify the walkingperiods, and to compute the subject's spatiotemporal parameters of gait;

FIGS. 5a-c demonstrate the operation of the algorithms in identifyingthe walking periods, and in computing the subject's spatio-temporalparameters of gait;

FIG. 6 is a flowchart of the algorithms used to detect and classify thelying posture;

FIG. 7 is a flowchart of the algorithm used to compute the subject'srisk of falling, and the quality of the subject's physical activity; and

FIG. 8 is a flowchart of the algorithm used to automatically detect thesubject's falls.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The present invention consists of a system and method for performing thefollowing tasks during the user's everyday life: (1) monitoring theuser's physical activity; (2) automatically detecting the user's falls;and (3) assessing the user's risk of falling. The second and third tasksare based on the results obtained from the first.

As shown by FIG. 1a , the system includes a sensing module (“SM”) 101for sensing, filtering and analyzing the user's 100 body movements. TheSM 101 is positioned on the user's 100 upper body (typically, on theuser's chest or torso), and is comprised of one to three accelerometers,each of which may be mono-axial or multi-axial. The only constraints onthe accelerometer configuration are that (1) accelerations in threeperpendicular directions must be measured; and (2) the accelerometer(s)is(are) configured to record accelerations in the frontal (F), vertical(V) and lateral (L) directions, which directions are relative to theuser 100 (see FIG. 1a ). In this document, all acceleration quantitiesare expressed in units of g (i.e., as multiples or fractions of g),where g is the gravitational constant equaling 9.81 m/s²: for example,by this convention an acceleration magnitude of 9.81 m/s² (in SI units)will be expressed 1.

The SM 101 may also include a data-storage system for storing themeasured accelerations. An optional on-board communications systemprovides the SM 101 the capability to transmit the collected data and/oranalyzed signals through either wired or wireless links for storageand/or for further offline analysis.

Analysis of the measured acceleration signals may be carried out (1)entirely on-board the SM 101, (2) partially on-board the SM 101 andpartially at other location(s), or (3) entirely at other location(s). Incase some or all of the analysis is (are) carried out on-board the SM101, a data processing circuit will be included on-board the SM to carryout the required computations according to software-based algorithmsdeveloped as part of the present invention. In case some or all of theanalysis is carried at location(s) separate from the SM 101, therequired data processing circuits performing the analysis may beordinary or special-purpose computers, and are integrated withsoftware-based algorithms developed as part of the present invention.

A. Monitoring the User's Physical Activity

Monitoring the user's physical activity consists of monitoring andassessing the user's postures, movements, trunk tilt, as well asfall-related task parameters. To this end, the system computes variousparameters associated with the subject's movement from the data recordedby the SM 101. These parameters consist of: (a) the subject's trunk tilt(specified in degree, measuring the angle between the subject's trunkaxis, and the axis aligned with the gravitational force-see FIG. 1b );(b) the type of the subject's postural transitions (PT); (c) the time ofthe subject's postural transitions; (d) the duration of the subject'spostural transitions; (e) the duration of the subject's locomotion; (f)characterization of the subject's locomotion (gait analysis); and (g)the type of subject's postures (e.g., sitting, standing, lying).

Use of accelerometers in place of gyroscopes by the present inventionallows for long-term autonomous operability of the system. Theassociated challenges introduced by this replacement, however, consistof processing the resulting noisy accelerometer signals during everydayliving activities.

I. Identifying the Types of Postural Transitions, and Computing theirDurations and Occurrences:

The flowchart in FIG. 2 and FIGS. 3a-3f demonstrate the operation of thealgorithms, developed as part of the present invention, used tocontinuously determine the type, time, and duration of the subject'spostural transitions (in this case, SI-ST and ST-SI) during everydaymovements. The algorithms use the frontal and vertical accelerometersignals—a_(F)(t) and a_(V)(t) respectively in FIG. 1a —where theirtime-varying nature is explicitly shown by including the time variable tin the notation used for these signals. In implementing the algorithms,the time variable t is by necessity discrete.

FIG. 3a shows an example of the acceleration patterns recorded by thevertical and frontal accelerometers from an elderly subject with a highrisk of falling (a_(V)(t): gray line 301; a_(F)(t): black line). Asidentified on the plot, the pattern consists of a sit-to-stand (SI-ST)postural transition followed by a period of walking and turning,followed by another postural transition (stand-to-sit; ST-SI).

As shown in FIG. 2, the algorithm performs the following steps on thefrontal accelerometer signal to determine the occurrence, duration andtype of the postural transitions:

-   -   1) segmenting, followed by wavelet filtering (box 201 in FIG. 2)        to remove signal artifacts induced by locomotion (e.g., walking,        climbing or descending the stairs, etc.)—see also the white        trace 305 in FIG. 3b , an example of the resulting filtered        signal a_(F-filt)(t);    -   2) locating the local maximum peaks (denoted by a_(F-p) 306 in        FIG. 3b ) in the filtered signal a_(F-filt)(t) 305 through a        peak-detection algorithm—this step corresponds to box 202 in        FIG. 2;    -   3) for each postural transition, corresponding to a particular        a_(F-p) 306, computing an initial estimate of the postural        transition duration (ΔT₁) by (boxes 203 and 204):        -   (i) determining whether a_(F-p) 306 is greater than a            pre-defined threshold Th₁;        -   (ii) if yes, locating the local minima 307 in a_(F-filt)(t)            305, within a specified time window, that precede and follow            the particular maximum peak a_(F-p) 306—see FIG. 3 b;        -   (iii) computing ΔT₁ 310 as the duration of the resulting            time interval I₁ separating the local minima computed above.

The above steps suppress and remove signal artifacts, such as noisypeaks, associated with shocks or other locomotion activities.

Following the initial determination of the postural transition duration(ΔT₁), the system computes a more accurate estimate of the posturaltransition duration, ΔT₂, by applying additional filters to the frontalacceleration signal only within a time interval that is centered at I₁,but that is typically 10% to 30% longer in duration than ΔT₁ 310. Suchfiltering of the frontal acceleration signal significantly decreases therequisite calculation costs, therefore enabling real-time implementationof the algorithm.

If the value ΔT₁ 310 surpasses a defined threshold, Th₂ (box 205 in FIG.2), the following steps are performed on the frontal accelerometersignal a_(F)(t) only during a time interval that is centered at I₁ butthat is typically 10% to 30% longer in duration:

-   -   1) as represented by box 206 in FIG. 2, low-pass filtering the        a_(F)(t) signal during the time interval I₁ by a wavelet;    -   2) as represented by box 207 in FIG. 2, locating the maximum        peak (a_(F-p2) 309) in the resulting filtered signal        a_(F-filt2)(t) 308 during time interval I₁ (see FIG. 3c );    -   3) within a specified time window, locating a local minimum in        a_(F-filt2)(t) closest to, and preceding, the particular maximum        peak a_(F-p2) (box 207 in FIG. 2);    -   4) within a specified time window, locating a local minimum in        a_(F-filt2)(t) closest to, and following the same maximum peak        (box 207 in FIG. 2);    -   5) computing ΔT₂ 311 (see FIG. 3c ) as the duration of the        resulting time interval I₂ separating the local minima computed        above (box 207 in FIG. 2);

The time of the maximum peak a_(F-p2) represents the time of thepostural transition, and the parameter ΔT₂ 311 represents the estimateof the duration of the postural transition.

For each postural transition, following the computation of its time ofoccurrence and its duration, the system uses the step-by-step algorithmbelow to identify its type (e.g., ST-SI or ST-SI):

-   -   1) as represented by boxes 209 and 210 in FIG. 2, for each        postural transition if ΔT₂ exceeds a predefined threshold Th₃,        estimate the trunk tilt angle in the sagittal plane, θ, using a        low-pass filtering of the a_(F)(t) signal during the        corresponding time interval I₂—since a_(F)(t) consists of a        θ-dependent gravitational component as well as a higher        frequency, pure frontal-acceleration component, low-pass        filtering removes the pure frontal-acceleration component,        leading to a quantity proportional to the sin(θ);    -   2) estimate the time-varying inertial frontal and vertical        accelerations a_(F-interial)(t) and a_(V-interial)(t) through        the following coordinate transformation (see box 211 in FIG. 2):

${\begin{bmatrix}{a_{F­{inertial}}(t)} \\{a_{v­{inertial}}(t)}\end{bmatrix} = {{\begin{bmatrix}{\cos\left( {\theta(t)} \right)} & {- {\sin\left( {\theta(t)} \right)}} \\{\sin\left( {\theta(t)} \right)} & {\cos\left( {\theta(t)} \right)}\end{bmatrix}\begin{bmatrix}{a_{F}(t)} \\{a_{V}(t)}\end{bmatrix}} - \begin{bmatrix}0 \\1\end{bmatrix}}},$

-   -   -   where, as mentioned before, the acceleration signal is            expressed in units of g (g represents the gravitational            constant (9.81 m/s²))—see also FIG. 1b for a free-body            diagram showing the inertial acceleration components;

    -   3) in parallel, apply an adequate, cascaded low-pass filter to        remove the artifacts from a_(V)(t), where the low-pass filter        functions as follows:        -   (i) removal of the gravitational component of a_(V)(t) 312            (FIG. 3e ) using the following equations (see also box 211            in FIG. 2):

a_(F)(t) = [a_(V − inertial)(t) + 1]sin (θ(t)) + a_(F − inertial)(t)cos (θ(t));a_(V)(t) = [a_(V − inertial)(t) + 1]cos (θ(t)) + a_(F − inertial)(t)sin (θ(t));${{a_{V - {filt}}(t)} = \sqrt{\left\lbrack {a_{F}(t)} \right\rbrack^{2} + \left\lbrack {a_{V}(t)} \right\rbrack^{2}}};$

-   -   -   (ii) low-pass filtering the resulting signal a_(V-filt)(t)            313, leading to a_(V-filt2)(t); and        -   (iii) filtering this signal by a moving-average filter to            obtain a_(V-filt3)(t) (see also box 212 in FIG. 2);

    -   4) as exemplified in FIGS. 3e-3f , determine the local peaks in        a_(V-filt3)(t) using a peak detection algorithm (box 213 in FIG.        2); the resulting positive and negative peaks—P_(max) 315 and        P_(min) 316, respectively—exceeding a predefined threshold Th₄,        are identified (boxes 214 and 215 in FIG. 2);

    -   5) classify the detected postural transition as sit-to-stand or        stand-to-sit through the sequence by which P_(max) and P_(min)        occur e.g., a Pa followed by a P_(min) identifies the postural        transition as a sit-to-stand pattern (box 316 in FIG. 2; see        also FIGS. 3e-3f );

    -   6) apply a post-processing algorithm to prevent        misclassification of postures and postural transitions: for each        postural transition, the classification as ST-SI or SI-ST will        be corrected based on the preceding and subsequent sequences of        postural transitions.        II. Analyzing Gait, and Identifying the Corresponding Walking        Periods:

FIG. 4 describes in flowchart form the software-based algorithm,developed as part of the invented system, to identify the subject'swalking periods and measure his or her gait parameters. Using datarecorded by the accelerometers, the algorithm can distinguish left andright gait steps, as well estimate the spatiotemporal gait parameters,e.g., swing, stance, double support, and gait speed.

The algorithm consists of the following steps:

-   -   1) remove from consideration data during time periods associated        with postural transitions and lying (boxes 401-402 in FIG. 4);    -   2) compute the time-varying norm (i.e., time-varying magnitude)        of the vertical and horizontal accelerometer signals as:

a_(F)(t) = [a_(V − inertial)(t) + 1]sin (θ(t)) + a_(F − inertial)(t)cos (θ(t));a_(V)(t) = [a_(V − inertial)(t) + 1]cos (θ(t)) + a_(F − inertial)(t)sin (θ(t));${{a_{V - {filt}}(t)} = \sqrt{\left\lbrack {a_{F}(t)} \right\rbrack^{2} + \left\lbrack {a_{V}(t)} \right\rbrack^{2}}};$where θ(t) represents the time-varying trunk angle, anda_(V-interial)(t) and a_(F-interial)(t) represent the time-varyingvertical and frontal acceleration components, respectively; FIG. 5bshows the resulting waveform, a_(V-filt3)(t) 503—see FIG. 1b for thefree-body diagram leading to the above formulas; these formulas allowfor suppression of the movement artifacts derived from the rotations ofthe subject's trunk;

-   -   3) remove the gravitational component from the vertical        acceleration signal in two steps: first, use formula stated in        step (2) to compute a_(V-filt3)(t) 503; second, as shown by box        403 in FIG. 4, band-pass filter the result, leading to        a_(V-filt4)-(t) 504 (see FIG. 5c );    -   4) as represented by box 404 in FIG. 4, identify gait steps as        the peaks 505 (see, FIG. 5c ) in the a_(V-filt4)(t) signal 504;    -   5) verify the sequence of the detected peaks according to        pre-defined conditions for gait patterns (box 405 in FIG. 4);    -   6) distinguish left and right steps (box 407 in FIG. 4) using        the signal a_(L)(t) from the lateral        accelerometer—specifically, (i) the subject's lateral velocity        v_(L)(t) is computed by integrating a_(L)(t) during the        recognized walking periods; (ii) the relationship between the        locations of the positive and negative peaks in v_(L)(t) with        the identified peak in the filtered vertical acceleration        signal, a_(V-filt4)(t) 504, allows for left and right steps be        distinguished.

This algorithm, furthermore, enables both the recognition of undetectedgait steps, and the removal of false detected steps.

The system, through another algorithm, computes the times of heel-strike(initial contact) and toe-off (final contact) events using informationextracted from the frontal and vertical acceleration signals—this stepcorresponds to box 408 in FIG. 4. Specifically, the local minimum andmaximum peaks in the frontal acceleration signal surrounding eachidentified vertical acceleration peak are used to identify heel-strikeevent and toe-off events. Following a heel-strike event, the subject'strunk continues to moves forward. As the toe-off event occurs, the trunkslows down, leading to a negative peak in the frontal accelerometersignal. Although a heel-strike event can be estimated using the verticalacceleration signal, when an impact is identified, the positive peak ofthe frontal acceleration pattern offers a significantly lesser noisysource for identification of the heel-strike event. Determination ofthese event times facilitates the measurement of the temporal parameters(e.g., stance, swing, double support, step time, gait cycle time, etc.)and other relevant information associated with the spatial parameters(i.e. stride velocity, step length and stride length).

Gait speed (i.e., stride velocity) is computed (box 410 in FIG. 4) usinginformation from the detected gait cycle and the amplitude ofacceleration during the double support.

III. Detecting and Classifying the Lying Posture.

The system distinguishes lying from sitting and standing by comparingthe angle of the vertical accelerometer signal a_(V)(t) to that of thegravitational component. While the vertical accelerometer measuresalmost zero during lying periods, its value is significantly greaterduring sitting and upright postures—in some cases the value is close tothe gravitational constant.

The system identifies both the sit/stand-to-lying (SI/ST-L) and themirror opposite (i.e., L-SI/ST) postural transitions using the followingalgorithm:

-   -   1) band-pass filter the vertical accelerometer signal (box 600        in FIG. 6);    -   2) calculate the gradient of the resulting the filtered signal        a_(V-filt5)(t) (box 601 in FIG. 6);    -   3) determine the maximum or minimum peak (P_(∇)) of this        gradient (box FIG. 6, box 602);    -   4) if the absolute value of the detected peak P_(∇) exceeds a        pre-defined threshold Th₅ (box 603, FIG. 6), estimate the        duration of lying postural transition using a local peak        detection scheme to identify peaks preceding (L_(initial)) and        following (L_(terminal)) P∇ (box 604, FIG. 6);    -   5) identify a lying posture at the time of the detected peak        when (i) the absolute value of the detected peak exceeds a        threshold Th₅ (box 603, FIG. 6); and (ii) the average value of        a_(V-filt5)(t) during the 3 seconds preceding the L_(initial) is        higher than a pre-defined threshold Th₆ (boxes 605-606, FIG. 6);        and (iii) the average value of a_(V-filt5)(t) during the 3        seconds following the L_(terminal) is lower than a threshold Th₇        (boxes 605-606, FIG. 6);    -   6) detect/identify a lying-to-sit/stand (L-SI/ST) postural        transition at the time of the detected peak (P_(∇)) when (i) the        absolute value of the detected peak exceeds a predefined        threshold Th₅ (box 603, FIG. 6); and (ii) the average value of        a_(V-filt5)(t) during the 3 seconds preceding the L_(initial) is        lower than Th₈ (boxes 605-607, FIG. 6); and (iii) the average        value of a_(V-filt5)(t) during the 3 seconds following the        L_(terminal) is higher than a threshold Th₉ (boxes 605-607, FIG.        6);    -   7) classify the lying posture further as lying on back, lying on        the front, or on the sides (left or right) on the basis of the        value of the frontal accelerometer signal (box 608, FIG. 6);    -   8) further classify lying on the side into lying on the right        and lying on the left according to the value of the lateral        accelerometer signal.        B. Computing the Risk of Falling and the Quality of the        Subject's Physical Activity

By monitoring the subject's physical activity, the invented system bothevaluates the quality of the subject's physical activity, and computesthe decline or progress in the subject's functional performance. FIG. 7presents the flowchart of the corresponding software-based algorithm,developed as part of the invented system.

The subject's risk of falling (RoF) during everyday life is computed byquantifying the quality of the subject's postural transitions andphysical activities using the following algorithm:

-   -   1) estimate the lateral sway (σ_(sway)) of the subject during PT        by computing the standard deviation of the lateral accelerometer        during PT (box 700, FIG. 7);    -   2) estimate the jerkiness in the subject's movement in all        directions (σ_(V-jerk), σ_(F-jerk), and σ_(L-jerk))—computed as        the standard deviation of the band-pass filtered acceleration        signals in the frontal, vertical and lateral directions (box        701, FIG. 7);    -   3) compute the mean (μ_(TD)) and standard deviation (σ_(TD)) of        the durations of the subject's postural transitions (ΔT₂), over        a day (box 702, FIG. 7);    -   4) compute the number of successive postural transitions        (N_(Succ_PT)) required for a subject to accomplish a single        task—an example is multiple unsuccessful attempts by a subject        to rise from a chair (box 703, FIG. 7);    -   5) evaluate the quality of physical activity by computing the        fraction of the time that subject has active posture (including        walking); the number of PTs per day; the number of walking        episodes during a day; and the longest continuous walking period        per day (boxes 704-706, FIG. 7);    -   6) evaluate the subject's risk of falling by inputting the above        parameters to a statistical model (e.g., stepwise) that provides        a linear combination of the calculated parameters to yield a        single score representative the subject's RoF (box 707, FIG. 7).        A subject is considered to be at a high-risk of falling if the        linear combination passes beyond a threshold, which may be        predefined, or may change adaptively.

To identify a subject at a high risk of falling more accurately, thesystem continually adjusts the requisite threshold values based on thehistory of falls or other similar events detected by the algorithm(e.g., high-impact experienced shortly after a postural transition, veryshort ST-SI durations, etc.)

I. Automatic Fall Detection.

The present invention uses a novel algorithm, based solely onaccelerometer signals, to automatically identify falls during thesubject's everyday life with high sensitivity and specificity. Thefall-detection algorithm described here uses information about thesubject's physical activity, as well as posture. The flowchart in FIG. 8describes in complete the algorithm developed to automatically detectthe subject's falls. The following summarizes the algorithm:

-   -   1) compute the norm (magnitude) of acceleration in the        transversal plane, a_(trans)(t) from the frontal and lateral        acceleration signals—a_(F)(t) and a_(L)(t),        respectively—through:        a _(trans)(t)=√{square root over ([a _(F)(T)]²+[a        _(V)(t)]²)}(box 800);    -   2) apply a peak-detection algorithm (box 801) to a_(trans)(t) to        identify the presence of “shocks” a_(trans)−P_(max);    -   3) confirm a fall event by considering the subject's PA and        posture prior to impact times (marked by the identified        shocks)—this step is carried out using algorithms described        above;    -   4) use different algorithms to identify a fall event, depending        on the results of step (3) supra:        -   (i) if impacts occur while subject is walking or turning,            depending on whether the impacts occurred after right or            left step, the algorithm chooses appropriate thresholds and            coefficients required for subsequent steps (Th₈: box 812;            Th₉: box 814; and coefficients of the multivariable model:            box 816);        -   (ii) if activity preceding the shock is not identified as            walking, turning or any sequential locomotion (e.g., walking            upstairs or downstairs,) the algorithm would identify as            fall events only the shocks that occur after a postural            transition to sitting or lying;        -   (iii) Next, thresholds and coefficients required for            subsequent steps are modified;    -   5) segment the shock-pattern following a postural transition        into pre-shock, impact, and post-shock phases based on the        location of local minimum peaks relative to the absolute maximum        peak (p_(max)) in the signal a_(trans)(t) (box 810, FIG. 8); the        set of thresholds chosen according to step (4) supra, and used        by the algorithm depends on whether the post-shock posture is        sitting or lying;    -   6) estimate the shock width (Δ_(shock)) using the local minimum        peaks before and after each the peak p_(max) (box 811, FIG. 8);        consider the peak to be an artifact and subsequently ignored if        its width does not exceed the threshold Th₈ (box 812, FIG. 8);    -   7) if the peak is not an artifact, compute the subject's speed        during the pre-shock phase by integrating the pattern of        vertical accelerometer—V_(V)(t) (box 813, FIG. 8); for the peak        to be recognized as a fall, the peak of the velocity profile        must exceed the threshold Th₉ (box 814, FIG. 8);    -   8) compute the following descriptors (box 815, FIG. 8):        -   (i) sum of all accelerations at the time of impact            t_(impact) as:            a _(total)(t _(impact))=a _(F)(t _(impact))+a _(V)(t            _(impact) +a _(V)(t _(impact));        -   (ii) the sum frontal and lateral accelerations at impact            time:            a _(F+L)(t _(impact))=a _(F)(t _(impact))+a _(L)(t            _(impact));        -   (iii) the difference of speed in each direction at the            impact time (V_(F-impact), V_(V-impact), and V_(L-impact));            and        -   (iv) energy of the norm of vertical and frontal acceleration            during the impact phase (Δ_(shock)):

${E_{Impact} = {\int_{\Delta{Shock}}\sqrt{{a_{F}(t)}^{2} + {{a_{V}(t)}^{2}{dt}}}}};$

-   -   9) identify a fall event through a multivariable model (stepwise        or linear combination) that uses the above descriptors as inputs        and coefficients chosen in step (4) supra (box 816, FIG. 8);    -   10) identify a fall as “serious” if the post-fall activities        represent an unusual activity pattern, such as a long-duration        rest, or multiple unsuccessful postural transitions (boxes        818-819, FIG. 8); in one embodiment of the invention, an alarm        will be set off following a “serious” fall.        II. Physical Activity Classification.

The algorithms described above will classify the subject's physicalactivity and posture, determine his or her risk of falling and qualityof movements. In addition, several rules will be applied to improve theclassifications performed by the above algorithms. These rules include,but are not limited to, the following:

-   -   1) If two contradictory states are detected (e.g., lying with        walking or sitting with walking) preference is first given to        lying, then to walking, and finally to postural transitions.        This rule is based on the rationale that the lying posture is        classified with the least amount of error. It should be noted        that since the algorithms for different postural detections        operate independently, two contradictory sets of activities may        be identified.    -   2) Two successive postural transitions classified as the same        type (e.g., SI-ST followed by SI-ST) are not possible—the        classifications are modified according to the preceding and        subsequent activities.    -   3) Elderly subjects cannot lean backwards after a SI-ST        transition with a high likelihood. The algorithm estimates the        trunk lean angle based on the trunk angle before (θ_(PT-pre))        and/or following (θ_(PT-post)) the postural transition.        -   (i) Both θ_(PT-pre) and θ_(PT-post) are estimated based on            the mean (E[.]) of the frontal acceleration during the rest            period immediately before, or after a postural transition,            according to the following formulas:            θ_(PT-pre)=sin⁻¹(E[a _(F)(t)|pre-PT-rest)            θ_(PT-post)=sin⁻¹(E[a _(F)(t)|post-PT-rest)            -   where E[a_(F)(t)pre-PT-rest] denotes the mean of the                frontal acceleration signal during the rest period                immediately before the postural transition;                E[aF(t)post-PT-rest] denotes the corresponding mean                after the postural transition.        -   (ii) If the standard deviation of both frontal and vertical            accelerations during a local interval before or after a            postural transition were lower than a pre-defined threshold,            the algorithm will classify that duration as a rest period.        -   (iii) Sensor inclination (θ_(initial)) is computed from the            average of the frontal accelerometer signal during a            recognized walking episode containing at least ten steps:            θ_(initial)=sin⁻¹([a_(F)(t)|walking; 10 steps].        -   (iv) The backwards-leaning state is detected if, subtracting            θ_(initial) from θ_(PT-pre) (or θ_(PT-post)) yields a value            lower than a pre-defined threshold.    -   4) The duration of the lying posture should be more than a        specified length (e.g., 30 seconds).    -   5) For an episode to be classified as “walking,” it must include        at least three successive steps within a predefined interval.    -   6) Since it is improbable for a person, especially an elderly        subject, to stand for long periods without any movements, long        standing periods without additional activity (e.g., more than        three minutes) are interpreted as sitting. This rule applies if        the standard deviations of both the vertical and frontal        accelerations are below pre-defined thresholds.

REFERENCES

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What is claimed is:
 1. A fall risk assessment system comprising: a dataprocessing system comprising one or more processor circuits configuredto process data generated by a sensor, the data including informationrepresentative of at least one signal generated by the sensor inresponse to movement of an upper part of a body of a person, the dataprocessing system programmed to at least; process said data to identifyone or more peaks in the at least one signal by comparing values of theat least one signal with one or more predefined fall thresholds; and forat least one identified peak of the one or more peaks: process said datato classify a non-fall activity performed by the person during a timeperiod based on the one or more predefined fall thresholds, the timeperiod that encompasses said non-fall activity being at least one ofbefore said identified peak or after said identified peak; and process aportion of the data corresponding to the time period that encompassessaid identified non-fall activity to compute at least one parameter ofthe following parameters using said data: a lateral sway of the person;an acceleration of the upper part of the person; or a duration of thenon-fall activity of the person; and the data processing system furtherprogrammed to: identify a risk of falling of the person at a subsequenttime based on an evaluation of the at least one computed parameter witha statistical model, the statistical model to produce a numerical scoreof the risk of falling based on the at least one computed parameter. 2.The fall risk assessment system of claim 1, wherein the acceleration ofthe upper part of the person is associated with acceleration in at leastone of a frontal direction or a vertical direction.
 3. The fall riskassessment system of claim 1, wherein the said non-fall activity is atleast one of standing, taking one step, taking multiple consecutivesteps forming an episode of walking, transitioning from sitting tostanding, or transitioning from standing to sitting.
 4. The fall riskassessment system of claim 1, wherein the risk of falling is identifiedby evaluating at least one of the following: a number of one or morenon-fall activities including the identified non-fall activity occurringduring the time period; or an average of the at least one computedparameter, the average computed based on said portion of the datacorresponding to the time period that encompasses said non-fall activityduring said time period.
 5. The fall risk assessment system of claim 4,wherein the number of one or more non-fall activities or the average ofat least one computed parameter is evaluated over one day.
 6. A fallrisk assessment system comprising: a data processing system comprisingone or more processor circuits configured to process data generated by asensor, the data including information representative of at least onesignal generated by the sensor in response to movement of a part of abody of a person, the data processing system programmed to at least;process said data to identify one or more peaks in the at least onesignal by comparing values of the at least one signal with one or morepredefined fall thresholds; and for at least one identified peak of theone or more peaks: process said data to classify a non-fall activityperformed by the person during a time period based on the one or morepredefined fall thresholds, the time period that encompasses saidnon-fall activity being at least one of before said identified peak orafter said identified peak; and process a portion of the datacorresponding to the time period that encompasses said identifiednon-fall activity to compute at least one parameter of the followingparameters using said data: an acceleration of the part of the body ofthe person; or a duration of the non-fall activity of the person; andthe data processing system further programmed to: identify a risk offalling of the person at a subsequent time based on an evaluation of theat least one computed parameter with a statistical model, thestatistical model to produce a numerical score of the risk of fallingbased on the at least one computed parameter.
 7. The fall riskassessment system of claim 6, wherein the acceleration of the part ofthe body of the person is associated with acceleration in at least oneof a frontal direction or a vertical direction.
 8. The fall riskassessment system of claim 6, wherein the said non-fall activity is atleast one of standing, taking one step, taking multiple consecutivesteps forming an episode of walking.
 9. The fall risk assessment systemof claim 6, wherein the risk of falling is evaluated by considering anaverage of the at least one computed parameter, the average computedbased on said portion of the data corresponding to the time period thatencompasses said non-fall activity during said time period.
 10. The fallrisk assessment system of claim 9, wherein the risk of falling isidentified by evaluating a number of one or more non-fall activitiesincluding the identified non-fall activity occurring during the timeperiod, wherein the number of one or more non-fall activities or theaverage of at least one parameter is evaluated during one day.
 11. Thefall risk assessment system of claim 6, wherein the sensor is attachedto an upper part of the body.
 12. The fall risk assessment system ofclaim 6, wherein the sensor is attached to a torso of the person or ashoulder of the person.
 13. The fall risk assessment system of claim 6,wherein the sensor is attached to an arm of the person.
 14. The fallrisk assessment system of claim 6, wherein said one or more processorcircuits of the data processing system are further programmed toidentify said risk of falling using a history of one or more fall eventsof the person.
 15. The fall risk assessment system of claim 14, whereinthe one or more processor circuits of the data processing system arefurther programmed to use at least one algorithm to detect one or morefalls of the person, and wherein said history of one or more fall eventsincludes a history of the detected one or more falls.
 16. The fall riskassessment system of claim 6, wherein said risk of falling is identifiedbased at least in part on a linear combination of two or more of said atleast one computed parameter.
 17. The fall risk assessment system ofclaim 6, further comprising a communications system configured toreceive said data from said sensor.
 18. The fall risk assessment systemof claim 6, further comprising the sensor.
 19. The fall risk assessmentsystem of claim 6, wherein said one or more processor circuits of thedata processing system are further programmed to identify said risk offalling based at least in part on one or more of: a percentage of timethe person is standing or walking; a number of postural transitions theperson attempts; and a ratio of a duration of time the person standingto a duration of time the person walking.
 20. The fall risk assessmentsystem of claim 6, wherein said movement of the part of the body of theperson is associated with activities of daily living.
 21. A method ofevaluating a risk of falling of a person, the method comprising:electronically receiving data generated by a sensor, the datarepresentative of at least one signal generated by the sensor inresponse to movement of an upper part of a body of a person; processingsaid data to identify one or more peaks in the at least one signal bycomparing values of the at least one signal with one or more predefinedfall thresholds; for at least one identified peak of the one or morepeaks; processing said data to classify a non-fall activity performed bythe person during a first time period based on the one or morepredefined fall thresholds, the first time period that encompasses saidnon-fall activity being at least one of before said identified peak orafter said identified peak; processing a portion of the datacorresponding to a the first time period that encompasses saididentified non-fall activity to compute at least one parameter of thefollowing parameters using said data: an acceleration of the upper partof the person; or a duration of the non-fall activity of the person; andidentifying a risk of falling of the person at a subsequent time basedon an evaluation of the at least one computed parameter with astatistical model, the statistical model to produce a numerical score ofthe risk of falling based on the at least one computed parameter.